arXiv:2606. 14218v1 Announce Type: cross Abstract: For robots to work safely in household environments, they need to be compliant and react to torque and force feedback during contact.
By Litian Liang, Jingxi Xu, Xinda Qi, Yujun Cai, Houzhu Ding, Luqi Wang, Zhixin Sun, Jyh-Herng Chow, Ming Yang, Mark Cutkosky
arXiv:2606. 00252v1 Announce Type: cross Abstract: Manipulating suspended payloads with humanoid robots is challenging because the robot can only influence an underactuated, oscillatory load through whole-body motion and intermittent contact.
By Songyang Liu, Shunyu Yao, Dingyuan Huang, Shuai Li
arXiv:2607. 19479v1 Announce Type: cross Abstract: Existing teleoperation systems are often tailored to specific robot hardware and task domains, limiting their scalability and adaptability.
By Joshua Citron, Renee Zbizika, Zeyi Liu, Shuran Song
The paper introduces InterTrack, a behavior world model that enables humanoid robots to perform robust whole-body tracking while interacting with varied terrain and objects. Using a Transformer architecture, InterTrack predicts actions, states, and behavior distributions conditioned on the environment, and it is trained with an automated pipeline that reconstructs 3D support geometry from retargeted motions. The system achieves an 81.3% success rate on terrain interaction, a 99.3% fall-recovery rate, and outperforms leading baselines in both free-space tracking and cross-terrain scenarios.
By Ziyang Cheng, Tianshu Tang, Jinxin Lan, Xinze Chen, Yuhan Gong, Zhichao Liu, Changzhong Wu, Yahao Mao, Zongyan Deng, Mingxuan Ma, Huasen Xi, Yilong Liu, Yutong Wu, Xiaofeng Wang, Borui Zhang, Bingyao Yu, Yang Wang, Yun Ye, Guan Huang, Xiaojie Jin, Zheng Zhu, Jiwen Lu
arXiv:2606. 14561v1 Announce Type: cross Abstract: Robotics manipulation research increasingly focuses on two-finger parallel grippers for their effectiveness, affordability, and ease of teleoperation.
By Francesco Capuano, Maximilian Eberlein, Fabrice Bourquin, Clemens Claudio Christoph
AdaptManip is a fully autonomous framework that enables humanoid robots to navigate, lift, and deliver objects without human demonstrations. It combines a recurrent state estimator for real‑time object tracking, a whole‑body locomotion policy with residual manipulation control, and a LiDAR‑based global position estimator. Trained entirely in simulation with reinforcement learning, the system achieves zero‑shot deployment on real hardware, outperforming imitation‑learning baselines in adaptability and success rate.
By Morgan Byrd, Donghoon Baek, Kartik Garg, Hyunyoung Jung, Daesol Cho, Maks Sorokin, Robert Wright, Sehoon Ha